Raft protocol-based Django multi-source data synchronization method and system

Abstract

The invention relates to the technical field of distributed computing, and discloses a Django multi-source data synchronization method and system based on a Raft protocol. According to the method, a Raft consensus algorithm is integrated in a Django framework to be designed into a non-intrusive middleware assembly, automatic discovery and dynamic management of multiple data source nodes are achieved, and data consistency and high availability of a distributed system are guaranteed through cooperative work of an election thread, a data synchronization thread and a period synchronization thread. The method supports comparison and synchronization of dynamic table structures, optimizes data synchronization performance through an incremental synchronization mechanism, and dynamically adjusts operation parameters through a hot update mechanism to adapt to requirements of different business scenes. According to the method, the consistency, flexibility and operation efficiency of the multi-source data synchronization system are remarkably improved, and the method is suitable for a multi-source database management scene in a distributed environment.

Description

Technical Field

[0001] This application relates to the technical field of database synchronization, and particularly to a distributed data synchronization technology based on the Django framework and the Raft consensus protocol. Background Art

[0002] In modern information systems, Django, as a popular Python Web framework, is widely used in the development of various Web applications. However, with the continuous expansion of business scale and the rapid growth of data volume, a single database often struggles to meet the system's requirements in terms of performance, capacity, and availability. Therefore, more and more application scenarios are starting to introduce the architecture design of multiple data sources, achieving goals such as load balancing, high availability, and elastic scalability by dispersing data storage across different database instances.

[0003] However, there are many technical challenges in implementing multi-source data synchronization under the Django framework. Firstly, the traditional master-slave replication mode is prone to introducing single points of failure, and when the master node fails, the data synchronization process often experiences significant delays. Secondly, the built-in ORM component of Django lacks native support for distributed transactions, making it difficult to ensure the ACID properties during the cross-data-source data synchronization process. In addition, the differences in table structures between different data sources also bring additional complexity to data synchronization, requiring developers to write a large amount of data conversion and mapping code, increasing the difficulty of development and maintenance.

[0004] Some existing distributed data synchronization solutions, such as distributed locks based on ZooKeeper and message queues based on Kafka, although alleviating the above problems to a certain extent, still have some limitations. For example, ZooKeeper, as an independent distributed coordination service, introduces additional deployment and operation and maintenance costs, and is prone to problems such as live locks and brain splits in abnormal situations such as network partitions. While the asynchronous replication solution based on message queues can improve the concurrency performance of the system, it often struggles to guarantee data consistency and real-time performance.

[0005] In summary, how to implement an efficient, reliable, and easily integrated multi-source data synchronization solution under the Django framework has become a key technical problem to be solved urgently. Summary of the Invention

[0006] The purpose of this application is to provide a Django multi-source data synchronization method and system based on the Raft protocol to solve the problems raised in the above background art.

[0007] This application discloses a Django multi-source data synchronization method based on the Raft protocol, including the following steps:

[0008] Integrate the Raft consensus algorithm into the Django framework as a middleware component. The middleware component automatically discovers and manages multiple data source nodes, assigns a unique identifier to each node, and assigns node roles according to the rules of the Raft protocol.

[0009] Unify the management of the running parameters of the Raft middleware component through a configuration file. The running parameters include the election timeout, heartbeat interval, and data synchronization interval, and the parameters support dynamic adjustment during runtime.

[0010] The Raft middleware component establishes a Raft cluster among multiple data source nodes. Each node in the cluster can be in the follower, candidate, or leader state.

[0011] In the Raft cluster, start three threads that work together, namely: an election thread, which is used to perform leader elections and maintain node states; a data synchronization thread, which is led by the leader node, receives data change requests, records the changes as log entries, and replicates the logs; a periodic synchronization thread, which periodically checks the data consistency of each node and performs supplementary synchronization.

[0012] Before data synchronization, the Raft middleware component, led by the leader node, compares the table structures of all nodes to ensure that the data schemas of all nodes are consistent.

[0013] The leader node records the log entries of the data change requests and replicates them to other nodes. After receiving the confirmation responses from more than half of the nodes, the data changes are committed and the execution results are returned.

[0014] In a preferred example, the Raft middleware component is implemented as a non-invasive module based on the Django framework, can be accessed into an existing Django project in a plug-in manner, and supports seamless integration with the Django ORM.

[0015] In a preferred example, the configuration file adopts the settings configuration format of Django, including: Raft node configuration, which is used to specify the network addresses and identifiers of the nodes; heartbeat configuration, which is used to set the preset heartbeat interval time; election configuration, which is used to set the minimum and maximum values of the election timeout; synchronization configuration, which is used to set the data synchronization interval.

[0016] In a preferred example, the running parameters support dynamic adjustment through a hot update mechanism. The hot update mechanism automatically adjusts the parameters by reading the changes in the configuration file without restarting the system.

[0017] In a preferred example, the node state transition rules in the Raft cluster are as follows: The initial state of all nodes is follower; A follower node converts to candidate after an election timeout and initiates a new round of election; A candidate node becomes the new leader after obtaining a majority of votes, and other nodes convert to followers; When the leader node fails or a higher term is detected, other nodes re-enter the candidate state and initiate an election.

[0018] In a preferred example, the election thread uses a randomized election timeout to avoid election conflicts caused by multiple candidate nodes initiating elections simultaneously.

[0019] In a preferred example, the execution steps of the election thread are as follows: When a node starts, it begins in the follower state and sets a random election timeout; If no heartbeat message is received within the timeout period, it converts to the candidate state and increments the term number; The candidate sends a vote request to other nodes, including the term number and log index information; Other nodes vote for the candidate when they meet the voting conditions; The candidate that obtains a majority of votes becomes the new leader and sends heartbeat messages; If the election times out, a new election starts.

[0020] In a preferred example, during the log replication process, the data synchronization thread attaches a unique sequence number and term number to each log entry to ensure the sequential consistency of log entries and prevent duplicate submissions.

[0021] In a preferred example, the execution steps of the data synchronization thread are as follows: After receiving a data change request, the leader generates a log entry; The leader replicates the log entry to other nodes; The follower adds the log entry to the local log and sends an acknowledgement; The leader commits the log entry after receiving a majority of acknowledgements; The leader notifies all nodes to commit the confirmed log entries.

[0022] In a preferred example, when the periodic synchronization thread detects inconsistent node data, it reduces the synchronization overhead through a differential comparison and incremental synchronization mechanism.

[0023] In a preferred example, the execution steps of the periodic synchronization thread are as follows: Periodically check the data consistency of each node; Compare and find the missing log entries; Pull the missing log entries from the leader node; Record errors when synchronization fails and retry in the next cycle.

[0024] In a preferred example, the table structure synchronization includes: The leader node checks the table structure version numbers of all nodes; When a version inconsistency is found, it generates a table structure synchronization log; Replicate the table structure synchronization log to other nodes; All nodes perform structure synchronization and update the version number.

[0025] In a preferred example, the log replication adopts an asynchronous replication mode, and after the local log writing is completed, the client response is preferentially returned, while asynchronously waiting for the confirmation of the majority of nodes.

[0026] In a preferred example, the method is applicable to a heterogeneous database environment and supports the unified synchronization management of relational and non-relational databases.

[0027] This application also discloses a Django multi-source data synchronization system based on the Raft protocol, including:

[0028] A middleware integration module, which is used to integrate the Raft consensus algorithm as a middleware component in the Django framework. The middleware component automatically discovers and manages multiple data source nodes, assigns a unique identifier to each node, and assigns node roles according to the rules of the Raft protocol;

[0029] A parameter management module, which is used to uniformly manage the running parameters of the Raft middleware component through a configuration file. The running parameters include the election timeout time, heartbeat interval, and data synchronization interval, and the parameters support dynamic adjustment during operation;

[0030] A cluster construction module, which is used to establish a Raft cluster among multiple data source nodes. Each node in the cluster can be in a follower, candidate, or leader state;

[0031] A thread management module, which is used to start three cooperating threads in the Raft cluster. The three threads include: an election thread, which is used to perform leader election and maintain node status; a data synchronization thread, which is led by the leader node, receives data change requests, records the changes as log entries, and performs log replication; a periodic synchronization thread, which periodically checks the data consistency of each node and performs supplementary synchronization;

[0032] A table structure synchronization module, which is used to compare the table structures of all nodes led by the leader node before data synchronization to ensure that the data schemas of all nodes are consistent;

[0033] A log replication module, which is used to record the log entries of data change requests by the leader node and replicate them to other nodes. After receiving the confirmation responses from more than half of the nodes, the data changes are committed and the execution results are returned.

[0034] By deeply integrating the Raft protocol with the Django framework, this application significantly improves the technical level of the multi-source data synchronization system in terms of consistency, high availability, and flexibility, and solves the technical problems of complex deployment, high data synchronization overhead, and insufficient dynamic adaptability in the prior art. Through the design of a non-invasive middleware component, this application can be plugged into the existing Django project environment to realize the automatic discovery and management of multi-source data nodes, simplify the system deployment and maintenance process, and reduce the transformation cost of the existing system.

[0035] The technical effects of this application are reflected in the following aspects: First, through the coordinated cooperation of the election mechanism, log replication mechanism, and periodic synchronization mechanism of the Raft protocol, the global consistency of data synchronization is ensured. The election thread adopts a randomized timeout mechanism, enabling the system to quickly complete the election of the leader node in a complex network environment and ensuring the high availability of the system in case of node failures or network partitions. During the log replication process, the leader node identifies log entries with a unique sequence number, and combined with the periodic synchronization thread's differential comparison and incremental synchronization operations on data inconsistencies, further optimizes the synchronization efficiency and reduces system resource consumption.

[0036] Second, this application realizes flexible adaptation to multi-source databases through the dynamic table structure comparison and synchronization mechanism. The leader node preferentially compares the table structure metadata of each node, identifies differences in field names, field types, and index information, and generates table structure synchronization log entries to ensure the reliability of table structure synchronization through a transaction mechanism. This dynamic synchronization method not only avoids system downtime problems that may occur during full-scale synchronization but also enhances the ability to ensure data schema consistency.

[0037] In addition, in terms of operating parameter management, by supporting the hot update mechanism and the dynamic adjustment function of the Django management background, this application enables the system to optimize core parameters such as election timeout, heartbeat interval, and data synchronization interval in real time according to the actual operating state, thereby further enhancing the flexibility and adaptability of the system. At the same time, this application introduces a thread pool management mechanism to reasonably allocate resources for election threads, data synchronization threads, and periodic synchronization threads, avoiding performance bottlenecks caused by resource competition between threads and ensuring the stable operation of the system in high-concurrency scenarios.

[0038] Finally, through the design of the fault handling mechanism, this application has achieved an efficient response to abnormal situations such as node failures, network partitions, and data synchronization failures. For example, in the scenario of network partitioning, minority nodes can automatically enter the read-only mode, and when the leader node recovers, the system can quickly synchronize the log entries missed during the failure to ensure data consistency and system integrity. Generally speaking, this application is characterized by low invasiveness, easy deployment, and high reliability, providing an efficient, flexible, and practical solution for multi-source data management under the Django framework.

[0039] A large number of technical features are recorded in the specification of this application, distributed in various technical solutions. If all possible combinations of technical features (i.e., technical solutions) of this application were to be listed, it would make the specification overly lengthy. To avoid this problem, each technical feature disclosed in the above-mentioned invention content of this application, each technical feature disclosed in the following embodiments and examples, and each technical feature disclosed in the drawings can be freely combined with each other to form various new technical solutions (these technical solutions are all regarded as having been recorded in this specification), unless the combination of such technical features is technically infeasible. For example, in one example, features A + B + C are disclosed, and in another example, features A + B + D + E are disclosed, and features C and D are equivalent technical means that play the same role. Technically, only one of them can be used and it is impossible to use both at the same time. Feature E can be combined with feature C technically. Then, the solution of A + B + C + D should not be regarded as having been recorded because it is technically infeasible, while the solution of A + B + C + E should be regarded as having been recorded. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic flowchart of a Django multi-source data synchronization method based on the Raft protocol according to the first embodiment of this application.

[0041] Figure 2 is a schematic structural diagram of a Django multi-source data synchronization system based on the Raft protocol according to the second embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In the following description, many technical details are presented to enable the reader to better understand this application. However, those of ordinary skill in the art can understand that even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in this application can still be implemented.

[0043] Explanation of some concepts:

[0044] Raft protocol. The Raft protocol is a distributed consensus algorithm designed to achieve data consistency and reliability in a distributed system through mechanisms such as leader election, log replication, and state machine consistency. The Raft protocol aims for understandability and simplifies the implementation and application of the protocol by decomposing the distributed consensus problem into multiple independent sub-problems.

[0045] Django framework. Django is a Web development framework based on the Python programming language, providing features such as rapid development, modular design, and high maintainability. The Django framework has built-in ORM (Object Relational Mapping), middleware, and plug-in mechanisms, which can simplify the Web development process and support seamless integration with multiple databases.

[0046] Middleware component. A middleware component refers to a module that runs between an application and the underlying system, responsible for processing data streams, performing logical operations, or providing extended functions. The middleware component in this application is a plug-in module based on the Raft protocol, which can be integrated into the Django framework in a low-invasive manner to implement distributed data synchronization and management functions.

[0047] Election thread. The election thread is a core module in the Raft protocol. It is responsible for triggering a new round of leader election through a randomized election timeout mechanism in the event of leader node failure or network partitioning, and ensuring the high availability and consistency of the distributed system.

[0048] Data synchronization thread. The data synchronization thread is a module used for log replication in the Raft protocol. The leader node uses this thread to record data changes as log entries and replicate them to other follower nodes to ensure data consistency across nodes in the distributed system.

[0049] Periodic synchronization thread. The periodic synchronization thread is a unique module designed in this application. It is responsible for periodically checking the data consistency of each node in the distributed system and fixing data inconsistency problems through differential comparison and incremental synchronization mechanisms, thereby further improving the reliability of the system.

[0050] Table structure comparison. Table structure comparison refers to detecting and fixing table structure differences by comparing the table structures (such as field names, field types, indexes, etc.) of databases on each node in the distributed system to ensure the schema consistency of multi-source data. In this application, the leader node generates table structure synchronization log entries to coordinate the table structure adjustment of each node.

[0051] Log entry. A log entry is the basic unit for recording data change operations in the Raft protocol, containing metadata of the change operations (such as term number, operation commands, etc.). The leader node ensures the consistency and traceability of data changes in the distributed system through the generation and replication of log entries.

[0052] Hot update mechanism. The hot update mechanism is a technology that can dynamically adjust system parameters without stopping the system operation. This application supports real-time adjustment of the Raft protocol running parameters (such as election timeout, heartbeat interval, and synchronization interval) through the hot update mechanism, thereby improving the flexibility and adaptability of the system.

[0053] Network partition. A network partition refers to a situation in a distributed system where communication between nodes is restricted due to network failures. This application ensures the high availability and data consistency of the system during network partitions through the fault tolerance mechanism of the Raft protocol and the read-only mode design of minority nodes.

[0054] Incremental synchronization. Incremental synchronization is a data synchronization strategy that avoids the performance overhead of full synchronization by only transmitting data differences or missing parts. The periodic synchronization thread in this application repairs data inconsistencies between nodes through the incremental synchronization mechanism, improving the synchronization efficiency of the system.

[0055] Transaction mechanism. The transaction mechanism refers to the technology of performing data operations in a distributed system based on the atomicity, consistency, isolation, and durability (ACID) principles. This application ensures that any failures or exceptions during table structure synchronization and log replication do not affect the consistency of the system through the transaction mechanism.

[0056] The following briefly describes some innovative points of this application:

[0057] Generally speaking, this patent application focuses on solving the problems of consistency, high availability, and dynamic adaptability in the multi-source data synchronization scenario under the Django framework, and proposes an innovative method based on the Raft protocol. Its technical concept is to deeply integrate the distributed consensus algorithm with the Web application framework, creating a non-invasive, pluggable middleware component that can achieve efficient and reliable data synchronization in a multi-data source environment. Compared with the prior art, this application does not simply apply the Raft protocol to the distributed system, but aims at the specific requirements of the Django framework. Through middleware design, it cleverly combines the election mechanism and log replication mechanism of the Raft protocol with the ORM operations, configuration management mechanism, and pluggable extension ability of Django, thereby realizing a low-invasive transformation of the existing system and significantly reducing the complexity of deployment and maintenance.

[0058] The creativity of this application is prominently reflected in the internal correlation and collaborative cooperation mode among its technical features. This cooperation mode is not a simple stacking, but a systematic technical design aiming at the core challenges of multi-source data synchronization, such as data consistency guarantee, dynamic table structure synchronization, node failure recovery, and system performance optimization. For example, the log replication mechanism of the Raft protocol generates a unique sequence number at the leader node and attaches it to each log entry. At the same time, combined with the periodic synchronization thread for incremental verification and repair of data consistency between nodes, a multi-level synchronization strategy is formed, which not only ensures the sequential consistency of log entries, but also reduces the performance overhead of data synchronization through incremental updates. In addition, the election thread adopts a randomized timeout mechanism, combined with the hot update ability of dynamic operating parameters, enabling the system to quickly complete leader election and resume data synchronization in complex scenarios such as node failure or network partition, thus achieving the high availability and robustness of the system.

[0059] It is worth mentioning that this application further enhances the adaptability and flexibility of multi-source data synchronization by introducing a dynamic table structure comparison and synchronization mechanism. Specifically, the leader node identifies the differences in field names, field types, and index information by comparing the table structure metadata of each node, and generates table structure synchronization log entries, ensuring data schema consistency while avoiding the system downtime problem that may be caused by traditional full-table structure synchronization operations. This mechanism is deeply integrated with the log replication process of the Raft protocol, forming a synchronization strategy that combines consistency guarantee and high efficiency.

[0060] In addition, this application has further optimized in terms of fault handling. By introducing a thread pool management mechanism, the system resources of the election thread, data synchronization thread, and periodic synchronization thread are reasonably allocated, avoiding the performance bottleneck caused by thread competition in traditional distributed systems. At the same time, when a network partition or node failure occurs, the system can maintain the read-only state of minority nodes, and quickly synchronize the missing data through the log replication mechanism of the leader node after the failure recovery, thus ensuring the integrity of data synchronization and the availability of the system.

[0061] Through the collaborative cooperation of the above technical features, this application realizes the high efficiency, consistency, and high availability of data synchronization while reducing the deployment complexity and maintenance cost, and is especially suitable for the multi-source data management scenario in a distributed database environment. This technical concept of deeply integrating the distributed consistency protocol with the Web framework starting from the actual needs of the Django framework and the specific technical effects obtained significantly exceed the ideas and capabilities of the prior art, reflecting the creativity of this application.

[0062] To make the purpose, technical solution, and advantages of this application clearer, the following will further describe the implementation manner of this application in detail with reference to the accompanying drawings.

[0063] The first embodiment of this application relates to a Django multi-source data synchronization method based on the Raft protocol, and its process is as Figure 1 shown. This method includes the following steps:

[0064] Step 100: Integrate the Raft consensus algorithm in the Django framework as a middleware component. The middleware component automatically discovers and manages multiple data source nodes, assigns a unique identifier to each node, and assigns node roles according to the rules of the Raft protocol.

[0065] Optionally, the Raft middleware component is implemented as a non-intrusive module based on the Django framework, can be accessed into an existing Django project in a plug-in manner, and supports seamless integration with the Django ORM.

[0066] Optionally, the Raft middleware component automatically discovers newly added data source nodes through regular scanning and heartbeat mechanism between nodes, and adds the newly added nodes to the Raft cluster through a consistency protocol to ensure the consistency of the cluster.

[0067] Specifically, the core of step 100 is to integrate the Raft consensus algorithm into the Django framework and implement the automated management of multi-source data nodes in the form of a middleware component. Through this step, the system can achieve distributed consistency management in a Django project in a low-intrusive manner, significantly reducing the technical complexity of applying the distributed consistency protocol to a Web development framework.

[0068] The design of the Raft middleware component is based on the Django framework and is implemented in a plug-in manner, with high flexibility and scalability. This non-intrusive design means that developers do not need to make complex modifications to the existing Django project to complete the integration. For example, developers can load the Raft middleware as an independent module into the Django project through simple configuration and installation. At the same time, this module can be seamlessly integrated with the Django's ORM (Object Relational Mapping), so as to directly combine the database operation interface of Django with the distributed consistency management function. This design avoids the high-intrusiveness problems of traditional distributed consistency tools (such as ZooKeeper or Etcd), while retaining the original development efficiency of the Django framework.

[0069] In terms of node discovery and consensus protocols, the Raft middleware component realizes the automatic discovery and management of data source nodes through regular scanning and heartbeat mechanisms between nodes. Specifically, the middleware component periodically sends heartbeat signals to detect the survival status of existing nodes and listens for join requests from newly added nodes. When a newly added node is discovered, the middleware component updates the cluster configuration according to the rules of the Raft protocol, adds the new node to the Raft cluster, and ensures data consistency between the newly added node and existing nodes through the log replication mechanism. For example, in a distributed database synchronization scenario, when a new database instance is added to the system, the middleware component automatically assigns a unique identifier to the instance and associates it with the database connection configuration of Django ORM, thus realizing the automatic management of the new node.

[0070] The assignment of node roles is based on the rules of the Raft protocol and dynamically determines the role distribution of leader nodes and follower nodes in the cluster through an election mechanism. The leader node is responsible for processing data change requests and replicating the change records to other nodes, while the follower nodes receive and apply the log updates from the leader node. Through this role assignment, the cluster can achieve a reasonable distribution of workloads among nodes and ensure data consistency. For example, in a three-node Raft cluster, one of the nodes is elected as the leader, responsible for processing all write operation requests, while the other two nodes act as followers, responsible for synchronizing and confirming the log entries of the leader.

[0071] In addition, the combination of regular scanning and heartbeat mechanisms can effectively handle the dynamic changes of nodes and the uncertainties of the network environment. For example, when a certain node fails or goes offline, the heartbeat mechanism can quickly detect the status change of the node and remove the node from the cluster through the consensus protocol; when the failed node recovers or rejoins, the middleware component will automatically trigger the consistency check and log supplementary synchronization to ensure that the node can rejoin the cluster and resume normal operation. Through this mechanism, the system can maintain high availability and consistency in scenarios with dynamic node changes.

[0072] In summary, step 100 integrates the Raft consensus algorithm in the Django framework and realizes the automatic management and consistency guarantee of multi-source data nodes in the form of a middleware component. This design not only reduces the application threshold of the distributed consistency protocol in the Web framework but also improves the flexibility of the system in a plug-in and non-invasive manner, laying a solid foundation for subsequent data synchronization and consistency operations. At the same time, through the collaborative work of the node discovery mechanism and the consensus protocol, the system can dynamically adapt to node changes and meet the high-availability requirements in a distributed environment.

[0073] Step 200: Uniformly manage the running parameters of the Raft middleware component through a configuration file. The running parameters include the election timeout, heartbeat interval, and data synchronization interval, and these parameters support dynamic adjustment during runtime.

[0074] Optionally, the configuration file adopts the settings configuration format of Django and includes:

[0075] · Raft node configuration, used to specify the network address and identifier of the node;

[0076] · Heartbeat configuration, used to set the preset heartbeat interval time;

[0077] · Election configuration, used to set the minimum and maximum values of the election timeout;

[0078] · Synchronization configuration, used to set the data synchronization interval.

[0079] Optionally, the running parameters support dynamic adjustment through a hot update mechanism. The hot update mechanism automatically adjusts the parameters by reading the changes in the configuration file without restarting the system.

[0080] Optionally, the running parameters support dynamic adjustment through the Django administration backend interface and can take effect in real time without interrupting the system operation.

[0081] Specifically, the core of Step 200 lies in uniformly managing the running parameters of the Raft middleware component through a configuration file and providing the ability for dynamic adjustment during runtime. This design greatly improves the flexibility and adaptability of the system, enabling the Raft middleware to be quickly adjusted according to actual needs in different scenarios without interrupting the system operation or manually restarting the service.

[0082] Explanation of Running Parameter Management

[0083] The running parameters of the Raft middleware component mainly include the election timeout, heartbeat interval, and data synchronization interval. These parameters directly affect the core functions of the Raft protocol and the stability of the system. For example, the election timeout determines the time interval for triggering a new round of elections after the leader node fails, the heartbeat interval is used to maintain communication between the leader and followers, and the data synchronization interval controls the frequency of log synchronization and consistency checks. By uniformly managing these parameters, the system can flexibly adjust the balance between performance and reliability in different network environments and business scenarios.

[0084] For the convenience of integration and use, this application uses the settings configuration format of Django to define these parameters. The configuration file format of Django is concise and easy to expand. Developers can manage the running parameters of the Raft middleware by adding the following structured configuration items to the settings.py file:

[0085] Raft node configuration: Specify the network addresses and unique identifiers of each node in the cluster so that the middleware can identify and manage each data source node.

[0086] Heartbeat configuration: Set the time interval for the leader node to send heartbeat signals to the follower nodes. For example, send a heartbeat every 50 milliseconds to ensure real-time communication between nodes in the cluster.

[0087] Election configuration: Define the minimum and maximum values of the election timeout. For example, set the timeout range to 150 milliseconds to 300 milliseconds to reduce the possibility of election conflicts.

[0088] Synchronization configuration: Set the time interval for data synchronization. For example, trigger a periodic synchronization thread every 1 second to check data consistency and supplement synchronization.

[0089] This configuration management method is clear and highly extensible. Developers can adjust the parameters according to specific requirements, thus providing convenience for system performance optimization.

[0090] Explanation of the dynamic adjustment mechanism

[0091] To further improve flexibility, the Raft middleware component supports dynamic adjustment of running parameters, that is, parameters can be modified in real time during system operation without restarting the service. This function is implemented through a hot update mechanism. The hot update mechanism will periodically monitor changes to the configuration file. When it detects that the configuration file has been modified, the system will automatically read the new parameters and apply them to the runtime environment.

[0092] For example, operation and maintenance personnel can adjust the election timeout during system operation to adapt to changes in network latency, or modify the data synchronization interval to optimize the performance of the cluster, and all of this can be completed without interrupting the service. Such a design greatly reduces the complexity of operation and maintenance. Especially in a distributed environment, for scenarios that require frequent parameter adjustment to adapt to dynamic changes, the hot update mechanism is particularly important.

[0093] Explanation of the Django administration backend

[0094] In addition to adjusting parameters through configuration files, the Raft middleware component also supports dynamic parameter adjustment through the Django administration backend interface. The Django administration backend provides a friendly web interface where operation and maintenance personnel can directly modify parameters such as the election timeout, heartbeat interval, and data synchronization interval in the backend page and view the effectiveness of these parameters in real time.

[0095] The implementation of this function relies on the built-in management function of the Django framework. By adding a configuration model in the backend interface, the system can combine the management of running parameters with web operations. For example, an administrator can select a node in the backend interface and modify its heartbeat interval. After clicking "Save", the new parameter will be immediately passed to the runtime environment without modifying the code or restarting the service. This operation method reduces the requirements for technical personnel, making parameter adjustment no longer dependent on professional developers, thus improving the usability and operation efficiency of the system.

[0096] Furthermore, the technical features in step 200 cooperate with each other to provide comprehensive support for the efficient operation of the system. The unified management of configuration files simplifies the maintenance and extension of parameters, and the combination of the hot update mechanism and the Django administration backend further enhances the flexibility and real-time nature of parameter adjustment. For example, in a high-concurrency scenario, the system administrator can adjust the data synchronization interval through the backend interface or configuration file to cope with the load pressure, and the hot update mechanism will automatically apply the new parameter to optimize the system performance. In addition, there is a close relationship among the election parameters, heartbeat parameters, and synchronization parameters. By reasonably configuring these parameters, the system can achieve a balance between performance and consistency and ensure reliability in a distributed environment.

[0097] For example, in a Raft cluster with five nodes, an administrator can adjust the parameters in the following ways to optimize system behavior:

[0098] Adjust the heartbeat interval through the configuration file: In an environment with low network latency, adjust the heartbeat interval from 100 milliseconds to 50 milliseconds to more quickly detect changes in the leader node status. The hot update mechanism will automatically apply this adjustment after the configuration file is modified.

[0099] Adjust the election timeout through the backend interface: When some nodes frequently conduct elections due to network partitioning, the administrator can adjust the election timeout range from 150 - 300 milliseconds to 200 - 400 milliseconds in the Django administration backend interface to reduce election conflicts.

[0100] Dynamically adjust the data synchronization interval: In scenarios where log changes are frequent, the administrator can set the data synchronization interval to 500 milliseconds through the background interface to trigger consistency checks more frequently, thereby reducing data synchronization latency.

[0101] In summary, step 200 provides a highly controllable operating environment for the Raft middleware component through unified configuration management and flexible dynamic adjustment mechanisms, not only improving the adaptability and convenience of the system, but also significantly reducing the operation and maintenance costs, laying a solid foundation for subsequent distributed data synchronization operations.

[0102] Step 300: The Raft middleware component establishes a Raft cluster among multiple data source nodes, and each node in the cluster can be in a follower, candidate, or leader state.

[0103] Optionally, the node state transition rules in the Raft cluster include:

[0104] · All nodes are initially in the follower state;

[0105] · A follower node transitions to a candidate after the election timeout and initiates a new round of elections;

[0106] · A candidate node becomes the new leader after obtaining a majority of votes, and other nodes transition to followers;

[0107] · When the leader node fails or a higher term is detected, other nodes re-enter the candidate state and initiate an election.

[0108] Optionally, the Raft middleware component determines the leader node in the cluster through an election mechanism to ensure that the leader node has priority control over data synchronization and log replication.

[0109] Specifically, the core of step 300 is to establish a cluster among multiple data source nodes through the Raft protocol, and to achieve consistency and high availability in the distributed system through node state management and the election mechanism. The Raft protocol defines clear node roles and state transition rules, enabling the distributed system to operate stably in the face of node state changes or failures, thus ensuring the reliability and efficiency of data synchronization.

[0110] Explanation of Node State Management

[0111] Each node in the Raft cluster can be in one of the following three states: follower, candidate, or leader. These three states are designed as a state machine that mutually transitions in the Raft protocol, and its transition rules ensure that the cluster can dynamically adapt to changes in node states, maintaining system consistency and high availability.

[0112] Initial state: Follower

[0113] All nodes are in the follower state by default at startup. Follower nodes will not actively initiate elections or process client requests, but will stay synchronized by receiving the heartbeat signal from the leader node. If the follower fails to receive the leader's heartbeat signal within a certain election timeout period, the leader is considered invalid and converted to the candidate state to initiate a new round of elections.

[0114] Candidate Status

[0115] When a node is converted to a candidate, it will increase its term number and send voting requests to other nodes to compete for the leader. The key task of the candidate state is to win the voting support of the majority of nodes. If the candidate obtains the majority of votes during the election, it will be converted to the leader; if the election fails or a message from a new leader is received during the election, it will return to the follower state.

[0116] Leader Status

[0117] When a candidate receives a majority of votes from the nodes, it becomes the new leader and is responsible for processing client data change requests and performing log replication. The leader node periodically sends heartbeat signals to all follower nodes to maintain cluster stability. If the leader fails or detects a higher term number, the other nodes re-enter the candidate state and initiate a new round of elections.

[0118] Through these states and transition rules, the Raft protocol can ensure that there is at most one leader node in the cluster at any time, thus avoiding the problem of multiple leader conflicts.

[0119] Description of the election mechanism

[0120] The election mechanism is one of the core functions of the Raft protocol, which is used to quickly select a new leader when the leader node fails to ensure the high availability of the cluster. The Raft middleware component implements the leader election through the following steps:

[0121] Election Trigger

[0122] When a follower node does not receive a heartbeat message from the leader within the election timeout, it automatically switches to the candidate state and initiates an election. To avoid conflicts caused by multiple candidates initiating elections at the same time, the Raft protocol uses a randomized election timeout (e.g., 150 milliseconds to 300 milliseconds) to reduce the possibility of election competition.

[0123] Voting Request

[0124] Candidate nodes send voting requests to other nodes in the cluster, asking for support to become the leader. The voting request contains the candidate's term number and log information so that other nodes can determine whether to support the candidate.

[0125] Voting response

[0126] Each node can vote only once in a term. If the candidate's term number is greater than the node's current term number and the candidate's log is at least as up-to-date as the local log, the node will vote for the candidate.

[0127] Majority vote confirmation

[0128] After a candidate node receives votes from a majority of nodes in the cluster, it successfully becomes the leader and starts sending heartbeat signals to notify other nodes of its leader status. If it fails to receive a majority of votes, the candidate will re-enter the follower state and wait for the next election.

[0129] Through this election mechanism, the Raft middleware component can quickly complete the transfer of leadership when the leader node fails, thus ensuring the high availability of the system. For example, in a three-node cluster, when the current leader node goes offline due to a network failure, the remaining two follower nodes will enter the candidate state according to the trigger mechanism of the election timeout, and finally elect a new leader to continue processing data synchronization tasks.

[0130] Furthermore, node state management and the election mechanism work closely together in the Raft protocol to jointly achieve the consistency and robustness of the distributed system. The node state transition rules provide clear trigger conditions and execution processes for the election mechanism, while the election mechanism maintains the consistency and high availability of the cluster through dynamic leadership transfer. For example, when the leader node fails, the follower nodes will quickly elect a new leader through the election mechanism, avoiding system interruption caused by leader failure. At the same time, node state management can ensure that all nodes always cycle among the three states of follower, candidate, and leader, thus avoiding confusion or rigidity in node states.

[0131] In addition, the election mechanism is also closely related to the log replication process. The newly elected leader needs to ensure that its log is at least consistent with that of a majority of nodes. Therefore, after being successfully elected, it will immediately start the log synchronization process to repair log inconsistencies caused by leader failures or network partitions. This mechanism further enhances the data consistency of the system.

[0132] For example, in a Raft cluster consisting of five nodes, when the system starts, all nodes are initially in the follower state. Suppose the current leader node fails due to a hardware fault. The remaining four follower nodes will enter the candidate state and initiate an election in sequence when they do not receive a heartbeat signal within the election timeout period. Since the election timeout period is random, it avoids the conflict of multiple candidates initiating an election simultaneously. Eventually, one of the candidate nodes is successfully elected as the new leader through a majority vote, starts receiving client requests and replicates the logs, while the other candidates return to the follower state.

[0133] In another scenario, assume that network latency causes some nodes to fail to receive the leader's heartbeat signal in a timely manner. These nodes may mistakenly think that the leader has failed and initiate an election. However, since the leader is actually still working, other nodes will refuse to vote for the candidate, thus avoiding the repeated assignment of the leader role. This mechanism demonstrates the robustness of the Raft protocol in handling network partitions and node failures.

[0134] In summary, step 300 realizes the dynamic management of node states and the leader node election mechanism by establishing a Raft cluster among multiple data source nodes, thus ensuring the consistency and high availability of the system. Node state management provides clear role division and conversion rules for the normal operation of the system, while the election mechanism quickly completes the leadership transfer when the leader fails, avoiding system interruption. The coordinated work of the two lays a solid foundation for data synchronization and consistency maintenance in a distributed environment, and at the same time enhances the adaptability and fault tolerance of the system in complex network scenarios.

[0135] Step 400: In the Raft cluster, start three threads that work together, namely:

[0136] · An election thread, used to perform leader election and maintain node states;

[0137] · A data synchronization thread, led by the leader node, receives data change requests, records the changes as log entries and replicates the logs;

[0138] · A periodic synchronization thread, regularly checks the data consistency of each node and performs supplementary synchronization.

[0139] Optionally, the election thread uses a randomized election timeout period to avoid election conflicts caused by multiple candidate nodes initiating an election simultaneously.

[0140] Optionally, the execution steps of the election thread include:

[0141] · When the node starts, it begins in the follower state and sets a random election timeout period;

[0142] · When no heartbeat message is received within the timeout period, it converts to the candidate state and increments the term number;

[0143] · The candidate sends a vote request to other nodes, including the term number and log index information;

[0144] · Other nodes vote for the candidate when they meet the voting conditions;

[0145] · The candidate who obtains a majority of votes becomes the new leader and sends heartbeat messages;

[0146] · If the election times out, the election restarts.

[0147] Optionally, during the log replication process, the data synchronization thread attaches a unique sequence number and term number to each log entry to ensure the sequential consistency of log entries and prevent duplicate submissions.

[0148] Optionally, the execution steps of the data synchronization thread include:

[0149] · After receiving a data change request, the leader generates a log entry;

[0150] · The leader replicates the log entry to other nodes;

[0151] · The follower adds the log entry to the local log and sends an acknowledgment;

[0152] · After receiving a majority of acknowledgments, the leader commits the log entry;

[0153] · The leader notifies all nodes to commit the confirmed log entries.

[0154] Optionally, when the periodic synchronization thread discovers inconsistent node data, it reduces the synchronization overhead through a differential comparison and incremental synchronization mechanism.

[0155] Optionally, the execution steps of the periodic synchronization thread include:

[0156] · Periodically check the data consistency of each node;

[0157] · Compare and find the missing log entries;

[0158] · Pull the missing log entries from the leader node;

[0159] · When the synchronization fails, record the error and retry in the next cycle.

[0160] Optionally, the three threads working together are managed by a thread pool to ensure reasonable resource allocation and avoid performance bottlenecks caused by resource conflicts.

[0161] Specifically, the core of step 400 lies in implementing key functions such as leader election, log replication, and node data consistency check in the Raft cluster by starting three threads that work together (the election thread, the data synchronization thread, and the periodic synchronization thread). Through clear division of responsibilities and coordinated cooperation among these threads, the high availability, consistency, and operating efficiency of the system are ensured. At the same time, thread pool management further optimizes resource allocation and interoperability among threads, avoiding performance issues caused by resource conflicts.

[0162] Explanation of the Election Thread

[0163] The election thread is responsible for implementing leader election and node status maintenance in the Raft cluster and is the core mechanism for the high availability of the cluster. Its main task is to quickly elect a new leader through the election process when the leader node fails, ensuring that the system can continue to run.

[0164] Function of Randomizing the Election Timeout

[0165] The election thread uses a randomized election timeout to reduce the probability of election conflicts caused by multiple candidate nodes in the cluster initiating elections simultaneously. For example, in a five-node cluster, the system will set a randomly generated election timeout between 150 milliseconds and 300 milliseconds for each node. If the leader node fails, each node enters the candidate state after the timeout. Due to the randomness of the timeout, the node that reaches the timeout first is more likely to become a candidate, thus reducing election conflicts.

[0166] Execution Steps of the Election Thread

[0167] When a node starts, it begins in the follower state and sets a random election timeout.

[0168] If no heartbeat message from the leader is received within the timeout, the node transitions to the candidate state and increments its local term number.

[0169] The candidate node sends a vote request to other nodes, which includes the candidate's term number and log index information.

[0170] Other nodes will vote for the candidate when the voting conditions are met (such as the candidate having a higher term number or the log being at least consistent with the local one).

[0171] After the candidate node receives votes from a majority of nodes, it becomes the new leader and starts sending heartbeat messages to the cluster to maintain its leader status.

[0172] If the candidate fails to receive a majority of votes within the election timeout, it re-enters the election process.

[0173] Through the above steps, the election thread can quickly complete the transfer of leadership and avoid system interruptions caused by the failure of the leader node.

[0174] Description of the data synchronization thread

[0175] The data synchronization thread is led by the leader node and is used to receive client data change requests and synchronize the change records to other follower nodes through the log replication mechanism. This is the key to achieving cluster data consistency and availability.

[0176] Guarantee of the uniqueness of log entries

[0177] During the log replication process, the data synchronization thread attaches a unique sequence number and term number to each log entry. This design can ensure the sequential consistency of log entries and prevent duplicate submissions. For example, when the leader node receives a write request, it generates a unique sequence number for it and attaches it to the log entry to ensure that the log entries are in the same order on all nodes.

[0178] Execution steps of the data synchronization thread

[0179] After receiving the data change request, the leader node generates the corresponding log entry and records it in the local log.

[0180] The leader copies the log entry to other follower nodes and waits for the confirmation responses from these nodes.

[0181] After receiving the log entry, the follower node adds it to the local log and sends a confirmation message to the leader.

[0182] After receiving confirmations from the majority of nodes, the leader node commits the log entry and marks it as committed.

[0183] Finally, the leader notifies all nodes to commit the confirmed log entries to ensure the consistency of the cluster.

[0184] Through this process, the data synchronization thread can efficiently complete the log replication and commit operations while ensuring consistency.

[0185] Description of the periodic synchronization thread

[0186] The task of the periodic synchronization thread is to periodically check the data consistency of each node and repair it through the differential comparison and incremental synchronization mechanism when inconsistencies are found. Compared with full synchronization, the incremental synchronization mechanism can significantly reduce the synchronization overhead and improve system efficiency.

[0187] Differential comparison and incremental synchronization mechanism

[0188] The periodic synchronization thread compares the log indexes and status information of each node, finds missing or inconsistent log entries, and pulls the missing log entries from the leader node. For example, if a follower node fails to synchronize logs in a timely manner due to a network failure, the periodic synchronization thread will find that the node's logs are behind during the next check and will only transfer the missing log entries through the incremental synchronization mechanism instead of resynchronizing all logs.

[0189] Execution steps of the periodic synchronization thread

[0190] Periodically check the data consistency of all nodes, including log indexes and committed status.

[0191] Compare the log entries of each node to find the missing or inconsistent parts.

[0192] Pull the missing log entries from the leader node and synchronize them to the lagging nodes.

[0193] If the synchronization fails (such as a network interruption), record the error message and retry in the next cycle.

[0194] This mechanism can not only maintain the consistency of cluster data, but also quickly supplement synchronization after node recovery to ensure the stable operation of the system.

[0195] Explanation of thread pool management

[0196] The coordinated work of the three threads (election thread, data synchronization thread, and periodic synchronization thread) requires reasonable resource allocation and management through a thread pool. Thread pool management can avoid resource competition among threads and improve system performance.

[0197] For example:

[0198] In a high-concurrency scenario, the thread pool can limit the maximum number of threads to prevent resource exhaustion caused by too many threads.

[0199] The thread pool dynamically allocates resources according to task priorities. For example, it gives priority to ensuring the execution of the election thread to quickly complete leader election.

[0200] Through the scheduling mechanism of the thread pool, reduce the overhead of thread creation and destruction and improve the system operation efficiency.

[0201] Furthermore, the division of labor among the three threads is clear but they cooperate with each other, jointly ensuring the consistency and high availability of the distributed system:

[0202] The election thread quickly completes the transfer of leadership through the election mechanism, providing a stable leader node for the system.

[0203] The data synchronization thread, under the leadership of the leader node, maintains the data consistency of the cluster through the log replication mechanism.

[0204] The periodic synchronization thread repairs the log differences between nodes through a periodic check and incremental synchronization mechanism, further enhancing the robustness of the system.

[0205] The introduction of the thread pool provides resource guarantee for the efficient operation of these threads, avoiding performance bottlenecks caused by resource competition.

[0206] For example, in a Raft cluster consisting of five nodes, assume that a follower node fails to synchronize the log in time due to network latency, and at the same time the leader node fails due to a fault:

[0207] The election thread will trigger a new leader election after the election timeout, and select a new leader.

[0208] The new leader node continues to process client requests through the data synchronization thread and copies the new log entries to other nodes.

[0209] The periodic synchronization thread discovers that the log of the follower node is behind, and pulls the missing log entries from the new leader node through the incremental synchronization mechanism to complete the repair of data consistency.

[0210] In summary, step 400 provides dynamic leader election, reliable data synchronization, and efficient data consistency check for the Raft cluster through the collaborative work of the election thread, data synchronization thread, and periodic synchronization thread. These threads, through a clear division of responsibilities and thread pool management mechanism, not only ensure the consistency and high availability of the system, but also improve the operating efficiency, providing reliable technical support for data synchronization in a distributed environment.

[0211] Step 500: Before data synchronization, the Raft middleware component is led by the leader node to compare the table structures of all nodes to ensure that the data modes of all nodes are consistent.

[0212] Optionally, the table structure synchronization includes:

[0213] · The leader node checks the table structure version numbers of all nodes;

[0214] · Generate a table structure synchronization log when the versions are inconsistent;

[0215] · Copy the table structure synchronization log to other nodes;

[0216] · All nodes execute structure synchronization and update the version numbers.

[0217] Specifically, the core of step 500 lies in ensuring the consistency of the data schema among all nodes in the Raft cluster through a table schema comparison and synchronization mechanism led by the leader node. This process is executed before data synchronization, aiming to eliminate issues such as data write conflicts, query failures, or system exceptions caused by inconsistent table schemas. Through the unified management and synchronization mechanism of the table schema, the system can maintain structural integrity and operational stability in a dynamically changing distributed environment.

[0218] Necessity of Table Schema Synchronization

[0219] In a distributed system, multiple data source nodes may have inconsistent database table schemas (such as field names, field types, indexes, or constraint conditions) due to version differences, network latency, or human configuration errors. This inconsistency can have a serious impact on data synchronization and operations. For example:

[0220] If a certain node lacks a certain field, a data change request for writing that field may fail, resulting in incomplete data.

[0221] If the table field types of some nodes are different, it may lead to data type errors or abnormal query results.

[0222] Inconsistencies in the table schema may also disrupt the correct application of log entries, resulting in inconsistent cluster states.

[0223] Therefore, comparing and fixing inconsistent table schemas before data synchronization is a crucial step in ensuring system consistency and correctness.

[0224] Specific Explanation of Table Schema Synchronization

[0225] Table schema synchronization is led by the leader node and includes the following steps:

[0226] Check the table schema version number

[0227] The table schema version number of each node is an identifier of the table schema status, usually maintained by the leader node. The leader node will send a table schema comparison request to all nodes in the cluster before synchronization and collect the version numbers and table schema information of each node. For example, if the table schema version number of node A is v1.0, while the version numbers of node B and node C are v1.1, the leader node will determine that the table schema of node A needs to be updated.

[0228] Generate a table schema synchronization log

[0229] If the leader node discovers that the table structure version numbers of some nodes are inconsistent with its own, it will generate a table structure synchronization log to record the table structure changes that need to be synchronized. For example, the log may contain the definition of new fields, changes in field types, or instructions for creating indexes. The process of generating the table structure synchronization log is usually based on differential comparison, that is, by comparing the table structure information of nodes, only the parts that need to be updated are recorded, thereby reducing the synchronization overhead.

[0230] Replicate the table structure synchronization log

[0231] The leader node copies the table structure synchronization log as a special log entry to other nodes. Similar to ordinary data synchronization logs, the table structure synchronization log also needs to ensure sequential consistency and majority node confirmation. For example, the leader node will send the synchronization log to Node A and Node B and wait for the confirmation responses from the majority of nodes.

[0232] Execute structure synchronization and update the version number

[0233] After receiving the table structure synchronization log, the follower node modifies the local table structure according to the instructions in the log. After the modification is completed, the node will update its own table structure version number and send a confirmation message to the leader node. After receiving the confirmation from the majority of nodes, the leader node marks the synchronization as completed and ensures that subsequent log entries can be applied to all nodes normally.

[0234] Furthermore, the entire process of table structure synchronization involves the leadership of the leader node, the log replication mechanism, and the local execution capabilities of the nodes. These technical features cooperate with each other to ensure the efficiency and reliability of the synchronization:

[0235] The leading role of the leader node

[0236] The leader node is responsible for checking, generating, and distributing the table structure synchronization log, playing the role of the cluster control center. This centralized control method avoids the complexity of direct communication between nodes and also ensures the sequentiality of table structure synchronization.

[0237] The guarantee of the log replication mechanism

[0238] The table structure synchronization log is distributed through the log replication mechanism of Raft, ensuring the consistency of the synchronization log among nodes. Even in the case of node failures or network partitions, the log replication mechanism can ensure the reliable transmission of the synchronization log through retry and confirmation mechanisms.

[0239] The atomicity of table structure updates

[0240] When a node applies the table structure synchronization log, it usually executes in a transactional manner to ensure the atomicity of the structure update. The failure of any intermediate step will not affect the integrity of the overall structure update.

[0241] For example, assume that in a three - node Raft cluster, the table structure version number of node A is v1.0, and the version numbers of node B and node C are v1.1. The specific differences are as follows:

[0242] The table of node A lacks the field new_column, and the type of the field existing_column is VARCHAR(50).

[0243] The table of node B and node C has added the field new_column and updated the type of the field existing_column to VARCHAR(100).

[0244] In this case, the process of table structure synchronization is as follows:

[0245] The leader node checks the table structure version number

[0246] The leader node discovers that the version number of node A lags behind that of B and C, and there are differences in the table structure information.

[0247] Generate a table structure synchronization log

[0248] The leader node generates a synchronization log and records the following changes:

[0249] Add the field new_column with the type VARCHAR(50).

[0250] Modify the type of the field existing_column to VARCHAR(100).

[0251] Copy the synchronization log

[0252] The leader node copies the synchronization log to node A and waits for node A's confirmation.

[0253] Node A executes the synchronization

[0254] Node A sequentially executes the field addition and field type modification operations according to the synchronization log, and then updates the table structure version number to v1.1 and sends a confirmation message.

[0255] Synchronization completed

[0256] After receiving the confirmation message from node A, the leader node marks the table structure synchronization as completed.

[0257] After the above process, the table structure of node A is consistent with that of node B and C, and the table structure consistency of the cluster is restored.

[0258] Advantages of table structure synchronization

[0259] Dynamically adapt to changes

[0260] The table structure synchronization mechanism can dynamically adapt to the structural differences between nodes. Especially in scenarios where nodes are added or the table structure is upgraded, it significantly reduces the complexity of manual maintenance.

[0261] Reduce synchronization overhead

[0262] Through differential comparison and incremental update methods, the table structure synchronization avoids the high overhead of full synchronization and improves the synchronization efficiency.

[0263] Enhance system stability

[0264] Completing the table structure synchronization before data synchronization eliminates potential problems caused by inconsistent structures and ensures the stable operation of the system.

[0265] In summary, step 500 provides a reliable solution for data schema consistency in a distributed environment through a table structure comparison and synchronization mechanism led by the leader node. By checking version numbers, generating synchronization logs, replicating logs, and performing synchronization operations, the system can efficiently repair table structure differences between nodes and ensure the smooth progress of data synchronization. This mechanism not only improves the consistency and stability of the system but also provides strong support for dynamically changing distributed systems.

[0266] Step 600: The leader node records the log entries of data change requests and replicates them to other nodes. After receiving confirmation responses from more than half of the nodes, it submits the data change and returns the execution result.

[0267] Optionally, the log replication adopts an asynchronous replication mode. After completing the local log writing, it preferentially returns the client response and asynchronously waits for confirmation from the majority of nodes.

[0268] Optionally, the log entries are stored using an encryption mechanism to ensure data security and prevent tampering.

[0269] Specifically, the core of step 600 is to manage the data change requests in a logged manner through the leader node and synchronize them to other nodes in the form of log replication. This step ensures data consistency in the distributed system and ensures the reliable submission of logs through the majority node confirmation mechanism. During this process, the adoption of the asynchronous replication mode and the log entry encryption mechanism not only improves the performance of the system but also enhances data security.

[0270] Explanation of log replication

[0271] Log replication is a key mechanism for achieving data consistency in the Raft protocol. In step 600, the leader node is responsible for receiving data change requests sent by clients (such as insert, update, or delete operations), generating corresponding log entries, and replicating them to other nodes. The process of log replication is as follows:

[0272] Receive data change requests and generate log entries

[0273] When a client sends a data change request, the leader node encapsulates the request as a log entry. A log entry usually contains the following information: Log index: Identifies the sequential position of the log to ensure the sequential consistency of the log. Term number: Records the leader's term number to ensure that the log entry is consistent with the current leader's state. Operation content: Records the specific data change operation (such as an SQL statement or key-value pair). Verification information: Such as a checksum, used to verify the integrity of the log content.

[0274] Asynchronously replicate log entries

[0275] After completing the local log write, the leader node immediately returns a response to the client and simultaneously asynchronously sends the log entry to other follower nodes for replication. This asynchronous replication mode can significantly improve the system's performance because the leader does not need to wait for all nodes to complete replication before responding to the client. For example, in a five-node cluster, after the leader records the log locally, it can immediately respond to the client with "operation recorded", while the replication process continues in the background.

[0276] Wait for a majority of nodes to confirm

[0277] The leader node will wait for more than half of the nodes in the cluster (including itself) to confirm that the log entry has been successfully replicated. When a majority of nodes have successfully replicated the log, the leader node marks the log entry as committed, immediately applies the data change to the local database, and notifies all follower nodes to commit the log entry.

[0278] Return the execution result

[0279] After the log entry is committed, the leader node returns the final execution result to the client. For example, if the client requests to insert a record, the leader node will return a successful operation result after the log entry is committed.

[0280] Explanation of the asynchronous replication mode

[0281] In log replication, the asynchronous replication mode is the key to improving performance. Specifically, the asynchronous replication mode has the following characteristics and advantages:

[0282] Prioritize returning a client response

[0283] After completing the local log writing, the leader node will preferentially return a response to the client without waiting for other nodes to complete replication. This approach can significantly reduce the client's waiting time and improve the overall throughput of the system. For example, in a high-concurrency scenario, the leader node can handle multiple client requests simultaneously without being blocked by log replication waiting.

[0284] Asynchronous waiting for majority node confirmation

[0285] The replication process is completed asynchronously by background threads. The leader node only needs to wait for the confirmation of the majority of nodes to commit the log entry, rather than requiring all nodes to complete replication. This majority confirmation strategy improves the fault tolerance of the system. For example, in a five-node cluster, even if two nodes are temporarily unable to replicate the log due to network problems, the leader can still complete the commit after the remaining three nodes confirm the log.

[0286] Optimizing the balance between performance and consistency

[0287] The asynchronous replication mode achieves a good balance between performance and consistency. Although the client may receive a response before the log replication is completed, through the majority node confirmation mechanism, the system can still ensure data consistency. For example, if a follower node fails to synchronize the log due to a fault, after the node recovers, the leader will supplement the missing log entries through the periodic synchronization thread to ensure data consistency.

[0288] Explanation of log entry encryption

[0289] To enhance data security, in step 600, an encryption mechanism is adopted for storing log entries. The encryption mechanism can effectively prevent data from being tampered with or leaked during storage or transmission. Especially in a multi-node distributed environment, data security is particularly important.

[0290] Design of the encryption mechanism

[0291] When each log entry is generated, its content will be encrypted using an encryption algorithm. The encrypted log entry includes: Encrypted content: such as the ciphertext of data change operations or SQL statements. Encryption key identifier: used to select the correct key during decryption. Integrity check information: such as a hash value, used to verify whether the log entry has been tampered with.

[0292] Encrypted storage and transmission

[0293] Storage security: Before the log entries are stored in the local log files of the leader node and follower nodes, they have been encrypted. Even if the log files are accidentally leaked, it is difficult for attackers to interpret the content.

[0294] Transport Security: When log entries are replicated to other nodes over the network, they are still transmitted in ciphertext, further preventing man-in-the-middle attacks.

[0295] Advantages of Log Entry Encryption

[0296] Preventing Sensitive Data Leakage: For example, some log entries may contain user privacy information (such as names, addresses, bank accounts, etc.), and encrypted storage can effectively protect this data.

[0297] Preventing Log Tampering: Through the encryption and verification mechanism, the system can detect and reject any unauthorized modified log entries.

[0298] Furthermore, in the entire log replication process, the leader node plays a core role. As the control center in the cluster, the leader node is responsible for receiving data change requests from clients, encapsulating them into log entries, and coordinating the replication and submission of logs. By strictly following the generation and distribution order of log entries, the leader node ensures the order and consistency of log entries, thus providing guarantee for maintaining a consistent state among all nodes in the cluster. This leading role is the key for the Raft protocol to achieve high availability and consistency in a distributed environment.

[0299] The combination of the asynchronous replication mode and the majority node confirmation mechanism further optimizes the balance between system performance and consistency. During the log replication process, the leader node returns a response to the client immediately after completing the local log write, without waiting for the replication operation to complete, thus significantly reducing the client's waiting time and improving the system's throughput capacity. At the same time, through the majority node confirmation mechanism, the system can still ensure data consistency while performing asynchronous replication. Even if some nodes fail to complete the log replication in time due to network latency or faults, as long as more than half of the nodes confirm the log entry, the leader node can still commit the data change. This mechanism is particularly effective in high-concurrency scenarios, enabling the system to achieve a good balance between performance and reliability.

[0300] In addition, to enhance data security, an encryption mechanism is adopted during the generation and transmission of log entries. The encryption mechanism not only ensures the storage security of log entries but also prevents data from being stolen or tampered with during network transmission. By working in coordination with the log replication mechanism, the encryption mechanism ensures the integrity and confidentiality of log entries in a distributed environment, and can effectively resist potential threats both in the storage and transmission links. This design provides reliable data security guarantee in a distributed system, while enhancing the credibility and robustness of the system.

[0301] For example, assume a three - node Raft cluster (Node A is the leader, and Nodes B and C are followers). The client sends a data change request (such as inserting a record) to the leader Node A. The log replication process is as follows:

[0302] Generate log entry

[0303] After receiving the request, Node A encapsulates it as a log entry and encrypts the log content. The generated log entry contains the index number 1, the term number 5, and the encrypted operation content.

[0304] Asynchronous log replication

[0305] After saving the log entry locally, Node A immediately returns the response "Operation recorded" to the client and asynchronously sends the encrypted log entry to Nodes B and C.

[0306] Wait for majority confirmation

[0307] After receiving the log entry, Nodes B and C decrypt and verify the log content, then save the log entry locally and send a confirmation message to Node A. After receiving the confirmations from Nodes B and C, Node A marks the log entry as committed.

[0308] Commit the log and apply the change

[0309] Node A applies the committed log entry to the local database and notifies Nodes B and C to commit the log entry. After committing, Nodes B and C synchronously update the database.

[0310] In summary, step 600 ensures data consistency and security in a distributed system through the log replication process of the leader node. The asynchronous replication mode significantly improves the system performance, enabling it to quickly respond to client requests, while the majority - node confirmation mechanism ensures the reliable submission of log entries. At the same time, the log entry encryption mechanism enhances data security, effectively preventing data leakage and tampering. The collaborative work of these technical features provides an efficient and reliable solution for data synchronization in a distributed environment.

[0311] Optionally, the method is applicable to a heterogeneous database environment, supporting unified synchronization management of relational and non - relational databases.

[0312] Optionally, the data change uses transactional operations and does not commit the change when the log replication fails or the node response times out.

[0313] Optionally, it further includes the following fault - handling mechanisms:

[0314] · When a node fails, other nodes initiate a new round of election;

[0315] · Keep minority nodes in read-only state during network partitioning;

[0316] · Automatically synchronize missed data after the leader fails and recovers;

[0317] · Automatically retry and record error logs when synchronization fails.

[0318] Specifically, this method has broad adaptability and shows significant advantages especially in heterogeneous database environments. By supporting unified synchronization management of relational and non-relational databases, this method can meet the diverse requirements in complex distributed systems. In actual scenarios, organizations usually use different types of databases simultaneously, such as relational databases (e.g., MySQL, PostgreSQL) and non-relational databases (e.g., MongoDB, Redis). This method abstracts the data synchronization process, eliminates the interface differences between different databases, and thus realizes unified log replication and consistency management. This design greatly improves the flexibility of the system, enabling it to be seamlessly integrated and run in multiple database architectures, and meeting the high-efficiency data synchronization requirements in heterogeneous environments.

[0319] In terms of the fault handling mechanism, this method adopts a multi-level fault tolerance design to ensure high availability and consistency in the distributed environment. First, during the data change process, the system uses transactional operations to ensure that data is not committed until it is successfully replicated to the majority of nodes. Specifically, when log replication fails or node response times out, the leader node will roll back the change operation, thus avoiding data inconsistency problems caused by incomplete commits. This transactional operation mechanism provides the system with reliable fault isolation capabilities, enabling the system to maintain the overall data consistency even when some nodes fail.

[0320] In addition, this method also includes a series of specific handling mechanisms for node failures, network partitioning, and log synchronization failures to further improve the robustness and fault tolerance of the system:

[0321] Initiate a new round of elections when a node fails

[0322] When a node fails, the remaining nodes will quickly elect a new leader through the election mechanism of the Raft protocol to ensure the continuous operation of the system. For example, in the case of the leader node failure, the follower nodes will initiate an election within the election timeout period to select a new leader to take over the data synchronization task, thus avoiding system interruption caused by the leader's failure.

[0323] Keep minority nodes in read-only state during network partitioning

[0324] In a network partition scenario, to avoid data inconsistency, minority nodes will automatically switch to read-only mode. This design ensures that only majority nodes can continue to process data write requests, while minority nodes only retain the ability to read until the network partition issue is resolved and they rejoin the cluster. For example, if a three-node cluster is divided into two parts due to network partition, with one part containing one node and the other part containing two nodes, the single-node part will enter the read-only state, while the majority part continues to operate normally.

[0325] Data synchronization after leader failure recovery

[0326] When a leader node fails and recovers, the system will automatically synchronize the missing log entries of that node to its local log to ensure data consistency when the node rejoins the cluster. For example, if a node misses several log updates during a failure, it will pull these missing log entries from the current leader node after recovery, and then participate in the normal operation of the cluster after completing the data synchronization.

[0327] Automatic retry and error logging when synchronization fails

[0328] During the replication of log entries, if some nodes fail to complete synchronization successfully due to network latency or other issues, the system will automatically retry the synchronization operation and record the detailed information of the failure for subsequent diagnosis. For example, in a high-load scenario, some nodes may be temporarily unable to respond to log replication requests. The system will attempt to synchronize multiple times at regular intervals. If the retry fails, an error log will be recorded for the operations and maintenance personnel to troubleshoot the problem. This automatic retry and logging mechanism effectively reduces the probability of data synchronization failure caused by transient failures, and at the same time provides a clear basis for the operation, maintenance and optimization of the system.

[0329] Generally speaking, this method can adapt to heterogeneous database environments and ensure the high reliability and consistency of the system through a multi-level fault handling mechanism. Transactional operations provide a basic guarantee for data changes, while specific handling mechanisms for node failures, network partitions and log synchronization failures further enhance the fault tolerance of the system. These designs enable the system to operate stably in a complex distributed environment. Even when facing multiple fault scenarios, it can quickly recover and ensure data consistency and security.

[0330] The second embodiment of this application relates to a Django multi-source data synchronization system based on the Raft protocol, and its structure is as Figure 2 shown. The Django multi-source data synchronization system based on the Raft protocol includes:

[0331] Middleware integration module, which is used to integrate the Raft consensus algorithm as a middleware component in the Django framework. The middleware component automatically discovers and manages multiple data source nodes, assigns a unique identifier to each node, and assigns node roles according to the rules of the Raft protocol.

[0332] Parameter management module, which is used to uniformly manage the running parameters of the Raft middleware component through a configuration file. The running parameters include election timeout time, heartbeat interval, and data synchronization interval, and the parameters support dynamic adjustment during runtime.

[0333] Cluster construction module, which is used to establish a Raft cluster among multiple data source nodes. Each node in the cluster can be in a follower, candidate, or leader state.

[0334] Thread management module, which is used to start three cooperating threads in the Raft cluster. The three threads include: an election thread, which is used to perform leader election and maintain node status; a data synchronization thread, which is led by the leader node, receives data change requests, records the changes as log entries, and replicates the logs; a periodic synchronization thread, which periodically checks the data consistency of each node and performs supplementary synchronization.

[0335] Table structure synchronization module, which is used to perform table structure comparison on all nodes led by the leader node before data synchronization to ensure that the data schemas of all nodes are consistent.

[0336] Log replication module, which is used to record the log entries of data change requests by the leader node and replicate them to other nodes.

[0337] After receiving the confirmation responses from more than half of the nodes, submit the data change and return the execution result.

[0338] The first implementation manner is the corresponding method implementation manner of this implementation manner. The technical details in the first implementation manner can be applied to this implementation manner, and the technical details in this implementation manner can also be applied to the first implementation manner.

[0339] It should be noted that those skilled in the art should understand that the implementation functions of the various modules shown in the above implementation manner of the Django multi-source data synchronization system based on the Raft protocol can be understood with reference to the relevant descriptions of the aforementioned Django multi-source data synchronization method based on the Raft protocol. The functions of the various modules shown in the above implementation manner of the Django multi-source data synchronization system based on the Raft protocol can be implemented by a program (executable instructions) running on a processor or by specific logic circuits. If the above Django multi-source data synchronization system based on the Raft protocol in the embodiments of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), magnetic disks, or optical discs that can store program codes. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0340] Correspondingly, the embodiments of the present application also provide a computer storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the various method embodiments of the present application are implemented.

[0341] In addition, an embodiment of the present application further provides a Django multi-source data synchronization system based on the Raft protocol, which includes a memory for storing computer-executable instructions and a processor; the processor is configured to implement the steps in the above method embodiments when executing the computer-executable instructions in the memory. Among them, the processor may be a central processing unit (Central Processing Unit, abbreviated as "CPU"), or other general-purpose processors, digital signal processors (Digital Signal Processor, abbreviated as "DSP"), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as "ASIC"), etc. The aforementioned memory may be a read-only memory (read-only memory, abbreviated as "ROM"), a random access memory (random access memory, abbreviated as "RAM"), a flash memory (Flash), a hard disk or a solid-state drive, etc. The steps of the methods disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0342] All documents mentioned in the present application are considered to be integrally included in the disclosure of the present application so as to be used as a basis for modification when necessary. In addition, it should be understood that after reading the above disclosure of the present application, those skilled in the art can make various changes or modifications to the present application, and these equivalent forms also fall within the scope claimed by the present application.

Claims

1. A Django multi-source data synchronization method based on the Raft protocol, comprising the following steps: Integrate the Raft consensus algorithm as a middleware component in the Django framework, which automatically discovers and manages multiple data source nodes, assigns a unique identifier to each node, and assigns node roles according to the rules of the Raft protocol; The operation parameters of the Raft middleware component are uniformly managed through a configuration file, and the operation parameters include an election timeout, a heartbeat interval, and a data synchronization interval, and the parameters support dynamic adjustment during runtime; The Raft middleware component establishes a Raft cluster between multiple data source nodes, and each node in the cluster can be in a follower, candidate or leader state; In the Raft cluster, three threads working together are started, namely: an election thread, which is used to perform leader election and maintain node status; The data synchronization thread is led by the leader node, receives data change requests, records the changes as log entries, and performs log replication; the periodic synchronization thread regularly checks the data consistency of each node and performs supplementary synchronization; Before data synchronization, the Raft middleware component is led by the leader node to compare the table structures of all nodes to ensure that the data modes of all nodes are consistent; The leader node records the log entries of the data change request and replicates it to other nodes. After receiving confirmation responses from more than half of the nodes, the data change is committed and the execution result is returned.

2. The method according to claim 1, characterized in that The Raft middleware component is implemented as a non-intrusive module based on the Django framework, can be connected to existing Django projects in a plug-in manner, and supports seamless integration with Django ORM.

3. The method according to claim 1, characterized in that The configuration file adopts the Django settings configuration format, including: Raft node configuration, used to specify the network address and identifier of the node; heartbeat configuration, used to set the preset heartbeat interval; election configuration, used to set the minimum and maximum values ​​of the election timeout; synchronization configuration, used to set the data synchronization interval.

4. The method according to claim 1, characterized in that: The operating parameters support dynamic adjustment through a hot update mechanism, which automatically adjusts the parameters by reading changes in the configuration file without restarting the system.

5. The method according to claim 1, characterized in that The node state transition rules in the Raft cluster include: the initial state of all nodes is followers; the follower node is converted to a candidate after the election timeout and initiates a new round of elections; the candidate node becomes the new leader after obtaining a majority of votes, and the other nodes are converted to followers; when the leader node fails or detects a higher term, the other nodes re-enter the candidate state and initiate an election.

6. The method according to claim 1, characterized in that The election thread adopts a randomized election timeout to avoid election conflicts caused by multiple candidate nodes initiating elections at the same time.

7. The method according to claim 1, characterized in that The execution steps of the election thread include: when the node starts, it starts in the follower state and sets a random election timeout; when no heartbeat message is received within the timeout, it is converted to the candidate state and the term number is increased; the candidate sends a voting request to other nodes, including the term number and log index information; other nodes vote for the candidate when the voting conditions are met; the candidate who obtains the majority of votes becomes the new leader and sends a heartbeat message; if the election times out, the election is restarted.

8. The method according to claim 1, characterized in that: During the log replication process, the data synchronization thread adds a unique sequence number and term number to each log entry to ensure the sequential consistency of the log entries and prevent duplicate submission.

9. The method according to claim 1, characterized in that: The execution steps of the data synchronization thread include: the leader generates a log entry after receiving a data change request; the leader copies the log entry to other nodes; the follower adds the log entry to the local log and sends a confirmation; the leader submits the log entry after receiving a majority confirmation; the leader notifies all nodes to submit the confirmed log entry.

10. A Django multi-source data synchronization system based on the Raft protocol, characterized in that: include: A middleware integration module is used to integrate the Raft consensus algorithm in the Django framework as a middleware component. The middleware component automatically discovers and manages multiple data source nodes, assigns a unique identifier to each node, and assigns node roles according to the rules of the Raft protocol. A parameter management module, used to uniformly manage the operating parameters of the Raft middleware component through a configuration file, wherein the operating parameters include an election timeout, a heartbeat interval, and a data synchronization interval, and the parameters support dynamic adjustment during runtime; A cluster building module, used to establish a Raft cluster between multiple data source nodes, each node in the cluster can be in a follower, candidate or leader state; A thread management module is used to start three threads working together in the Raft cluster, including: an election thread, which is used to perform leader election and maintain node status; a data synchronization thread, which is led by the leader node, receives data change requests, records the changes as log entries and performs log replication; a periodic synchronization thread, which regularly checks the data consistency of each node and performs supplementary synchronization; The table structure synchronization module is used to compare the table structures of all nodes by the leader node before data synchronization to ensure that the data modes of all nodes are consistent; The log replication module is used by the leader node to record the log entries of data change requests and replicate them to other nodes. After receiving confirmation responses from more than half of the nodes, the data changes are submitted and the execution results are returned.

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